The present invention relates to computer related information search and retrieval, and specifically to multimedia and streaming media search tools.
An aspect of the Internet (also referred to as the World Wide Web, or Web) that has contributed to its popularity is the plethora of multimedia and streaming media files available to users. However, finding a specific multimedia or streaming media file buried among the millions of files on the Web is often an extremely difficult task. The volume and variety of informational content available on the web is likely continue to increase at a rather substantial pace. This growth, combined with the highly decentralized nature of the web, creates substantial difficulty in locating particular informational content.
Streaming media refers to audio, video, multimedia, textual, and interactive data files that are delivered to a user""s computer via the Internet or other network environment and begin to play on the user""s computer before delivery of the entire file is completed. One advantage of streaming media is that streaming media files begin to play before the entire file is downloaded, saving users the long wait typically associated with downloading the entire file. Digitally recorded music, movies, trailers, news reports, radio broadcasts and live events have all contributed to an increase in streaming content on the Web. In addition, less expensive high-bandwidth connections such as cable, DSL and T1 are providing Internet users with speedier, more reliable access to streaming media content from news organizations, Hollywood studios, independent producers, record labels and even home users themselves.
A user typically uses a search engine to find specific information on the Internet. A search engine is a set of programs accessible at a network site within a network, for example a local area network (LAN) or the Internet and World Wide Web. One program, called a xe2x80x9crobotxe2x80x9d or xe2x80x9cspiderxe2x80x9d, pre-traverses a network in search of documents (e.g., web pages) and builds large index files of keywords found in the documents. Typically, a user formulates a query comprising one or more search terms and submits the query to another program of the search engine. In response, the search engine inspects its own index files and displays a list of documents that match the search query, typically as hyperlinks. The user then typically activates one of the hyperlinks to see the information contained in the document.
Search engines, however, have drawbacks. For example, many typical search engines are oriented to discover textual information only. In particular, they are not well suited for indexing information contained in structured databases (e.g. relational databases), voice related information, audio related information, multimedia, and streaming media, etc. Also, mixing data from incompatible data sources is difficult for conventional search engines.
Another disadvantage of conventional search engines is that irrelevant information is aggregated with relevant information. For example, it is not uncommon for a search engine on the web to locate hundreds of thousands of documents in response to a single query. Many of those documents are found because they coincidentally include the same keyword in the search query. Sifting through search results in the thousands, however, is a daunting task. For example, if a user were looking for a song having the title xe2x80x9cI Am The Walrus,xe2x80x9d the search query would typically contain the word xe2x80x9cwalrus.xe2x80x9d The list of hits would include documents providing biological information on walruses, etc. Thus, the user would have to review an enormous number of these hits before finally (if ever) reaching a hit related to the desired song title. Adding to a user""s frustration is the possibility that many of the search results are duplicates and/or variants of each other, leading to the same document (e.g. uniform resource locator, URL). Further difficulty occurs in trying to evaluate the relative merit or relevance of concurrently found documents. The search for specific content based on a few key words will almost always identify documents whose individual relevance is highly variable.
Thus, there is a need for an automated media search tool that provides information to a user that overcomes the previously described drawbacks and disadvantages.
A method and system for searching for media on a computer network utilizes metadata. The media are searched for using at least one search term. Metadata associated with the media are extracted from the search results. This extracted metadata is enhanced. The search results are grouped into group(s), wherein extracted metadata associated with search results in a group have at least one common attribute. The method and system provide the media and/or the media uniform resource locator (URL).